Qualitative Analysis with Large-N: A New Method with an Application to Aspirations in Bangladesh

Abstract The qualitative analysis of open-ended interviews has vast potential in economics but has found limited use. This is partly because the interpretative, nuanced human reading of text and coding that it requires is labour intensive and very time consuming. This paper presents a method to simplify and shorten the coding process by extending a small sample of interpretative human-annotated interviews to a larger, representative, sample using supervised natural language processing. We extensively assess the robustness and reliability of this approach to show when and how it adds value, including an analysis of how many human-annotated documents are optimal for a budget constrained researcher. We apply this approach to analyse 2,200 open-ended interviews on parent’s aspirations for children with Rohingya refugees and their Bangladeshi hosts. We show that studying aspirations with open-ended interviews extends the economics focus on material goals to ideas from philosophy and anthropology that emphasize aspirations for moral and religious values, and the navigational capacity to achieve these aspirations. This approach allows us to identify several novel results including a new type of migrant selection; we show that Rohingya refugees are negatively selected on education but positively selected on navigational capacity.